The Mathematical Foundations of Deep Learning: From Rating Impossibility to Practical Existence Theorems

The Mathematical Foundations of Deep Learning: From Rating Impossibility to Practical Existence Theorems

Centre de recherches mathématiques - CRM via YouTube Direct link

Introduction

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1 of 25

Introduction

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Classroom Contents

The Mathematical Foundations of Deep Learning: From Rating Impossibility to Practical Existence Theorems

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  1. 1 Introduction
  2. 2 Collaborators
  3. 3 AI Index Report
  4. 4 AI Subfields
  5. 5 Impact of Deep Learning
  6. 6 Computational Mathematics and Deep Learning
  7. 7 Deep Learning Skepticism
  8. 8 Mathematical Problems for the Next Century
  9. 9 Presentation Structure
  10. 10 Deep Neural Networks
  11. 11 Research Question
  12. 12 Can Deep Learning generalize
  13. 13 The connectionist
  14. 14 Notation
  15. 15 General Results
  16. 16 Tau
  17. 17 Training History
  18. 18 Approximation
  19. 19 Approximation with orthogonal polynomials
  20. 20 Approximation techniques
  21. 21 In practice
  22. 22 Compressed sensing
  23. 23 Recap
  24. 24 Research directions
  25. 25 Conclusion

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